WO-2026097035-A1 - AUTOMATED MORTALITY RISK ASSESSMENT THROUGH BLOOD SMEAR IMAGES AND SUBJECT DATA

WO2026097035A1WO 2026097035 A1WO2026097035 A1WO 2026097035A1WO-2026097035-A1

Abstract

Presented herein are systems and systems of determining values indicating probabilities of conditions of subjects using blood samples. A computing system may receive a plurality of images of a blood sample having white blood cells (WBCs) and red blood cells (RBCs) obtained from a subject at a time. The computing system may identify, from the plurality of images of the blood sample, (i) a set of WBC images corresponding to the WBCs and (ii) a set of RBC images corresponding to the RBCs. The computing system may apply the set of WBC images and the set of RBC images to a machine learning (ML) architecture. The computing system may determine, based on applying the ML architecture, a value indicating a probability of mortality for the subject at the time interval relative to the time. The computing system may generate a classification of the first subject in accordance with the value.

Inventors

GOLDGOF, Greg
WEBB, Dylan
COLORADO-JIMENEZ, Cesar
SINGI, Siddharth
VANDERBILT, Chad
MCVOY, Lauren
FENELUS, Maly

Assignees

MEMORIAL SLOAN-KETTERING CANCER CENTER
MEMORIAL HOSPITAL FOR CANCER AND ALLIED DISEASES
SLOAN-KETTERING INSTITUTE FOR CANCER RESEARCH

Dates

Publication Date: 20260507
Application Date: 20251103
Priority Date: 20241104

Claims (20)

1. A method of determining values indicating probabilities of conditions of subjects using blood samples, comprising: receiving, by one or more processors, a first plurality of images of a first blood sample having first white blood cells (WBCs) and first red blood cells (RBCs) obtained from a first subject at a first time; identifying, by the one or more processors, from the first plurality of images of the first blood sample, (i) a first set of WBC images corresponding to the first WBCs and (ii) a first set of RBC images corresponding to the first RBCs; applying, by the one or more processors, the first set of WBC images and the first set of RBC images to a machine learning (ML) architecture, wherein the ML architecture is established using a plurality of examples, each of the plurality of examples comprising (i) a second set of WBC images of a second blood sample from a second subject at a second time, (ii) a second set of RBC images of the second blood sample from the second subject at the second time, and (iii) a label indicating one of mortality or survival at a time interval relative to the second time; determining, by the one or more processors, based on applying the first plurality of images to the ML architecture, a value indicating a probability of mortality for the first subject at the time interval relative to the first time; generating, by the one or more processors, a classification of the first subject as one of one of mortality or survival in accordance with the value indicating the probability of mortality; and storing, by the one or more processors, using one or more data structures, an association between the first subject and the classification.
2. The method of claim 1, wherein generating the classification further comprises generating the classification to indicate that the first subject is to survive for the time interval relative to the first time, responsive to the value indicating the probability of mortality not satisfying a threshold, and further comprising: Atty. Dkt. No.: 115872-3344 providing, by the one or more processors, an output identifying the classification to indicate that the first subject is to survive for the time interval relative to the first time.
3. The method of claim 1, wherein generating the classification further comprises generating the classification to indicate that the first subject is at risk of dying within the time interval relative to the first time, responsive to the value indicating the probability of mortality not satisfying a threshold, and further comprising: providing, by the one or more processors, an output identifying the classification to indicate that the first subject is at risk of dying within the time interval relative to the first time.
4. The method of claim 3, wherein providing the output further comprising providing, based on the classification, the output comprising at least one of: (i) a notification for a clinician to examine the first subject, (ii) a notification to administer an intervention within the time interval, or (iii) a notification for the first subject to request for medical attention.
5. The method of claim 1, further comprising receiving, by the one or more processors, a first nonimage dataset comprising at least one of (i) a first plurality of traits of the first subject, (ii) a first blood count derived from the first blood sample, (iii) a first plurality of parameters derived from testing of the first blood sample, or (iv) a first plurality of physiological measurements of the first subject, wherein applying to the ML architecture further comprises applying the first non-image dataset to the ML architecture, wherein at least one of the plurality of examples comprises a second non-image dataset comprising at least one of (i) a second plurality of traits of the second subject, (ii) a second blood count derived from the second blood sample, or (iii) a second plurality of measures derived from testing of the second blood sample, or (iv) a second plurality of physiological measurements of the second subject, wherein generating the classification further comprises generating the classification based on applying the first non-image dataset to the ML architecture. Atty. Dkt. No.: 115872-3344
6. The method of claim 1, wherein at least one of the plurality of examples further comprises the label identifying one of presence or absence of at least one of a plurality of conditions associated with the mortality in the second subject, wherein the plurality of conditions comprises a systemic inflammatory response syndrome (SIRS), sepsis, septic shock, bacterial infection, disseminated intravascular coagulation (DIC), microangiopathic hemolytic anemia (MAHA), acidosis, multiorgan failure, anemia, hemophagocytic lymphhistiocytosis, and cytokine release syndrome, and further comprising: determining, by the one or more processors, based on applying the first plurality of images to the ML architecture, a second value indicating a probability of a condition of the plurality of conditions associated with the mortality in the first subject, and wherein generating the classification further comprises generating the classification to identify one of a presence or absence of a condition of the plurality of conditions associated with the mortality in the first subject in accordance with the second value.
7. The method of claim 6, further comprising: generating, by the one or more processors, based on applying the first plurality of images to the ML architecture, a plurality of embeddings used to determine the second value; executing, by the one or more processors, using the plurality of embeddings, a clustering model comprising a plurality of clusters within a feature space, each of the plurality of clusters associated with a respective set of parameters for at least one of the plurality of conditions; determining, by the one or more processors, based on executing the clustering model, an assignment of the plurality of embeddings to a cluster of the plurality of clusters; and identifying, by the one or more processors, from the cluster associated with the assignment of the plurality of embeddings, a set of parameters for the condition of the plurality of conditions.
8. The method of claim 7, wherein at least one example of the plurality of examples comprises the label identifying a plurality of parameters derived from testing of the second blood sample, Atty. Dkt. No.: 115872-3344 wherein the clustering model is established by determining, for each cluster of the plurality of clusters, the respective set of parameters based on the plurality of parameters of the at least one example associated with a second plurality of embeddings assigned to the cluster.
9. The method of claim 1, further comprising: determining, by the one or more processors, a relative score indicating a difference between the first subject and a plurality of subjects based on the value indicating the probability of mortality for the first subject and a second value indicating a composite probability of mortality of a plurality of subjects; and identifying, by the one or more processors, from a plurality of categories, a category for the first subject in accordance with the relative score and a score category for the category.
10. The method of claim 1, further comprising providing, by the one or more processors, for presentation, a user interface comprising one or more of: (i) a category for the first subject, (ii) a relative score between the first subject and a plurality of subjects, (iii) the value indicating the probability of mortality for the first subject, (iv) a set of parameters for a condition, (v) the condition of the first subject.
11. The method of claim 1, wherein the first blood sample is acquired in accordance with peripheral blood smear (PBS), and the first plurality of images of the first blood sample is generated within a time of the PBS, wherein identifying the first set of WBC images and the first set of RBC images further comprises identifying at least one image from the first plurality of images as one of the first set of WBC images and the first set of RBC images based on a visual characteristic of the at least one image.
12. The method of claim 1, wherein the ML architecture further comprises: a WBC encoder configured to generate a set of WBC embeddings using the first set of WBC images, Atty. Dkt. No.: 115872-3344 an RBC encoder configured to generate a set of RBC embeddings using the first set of RBC images, a feature encoder configured to generate a set of feature embeddings using a non-image dataset, an aggregate predictor configured to determine the value indicating the probability of mortality for the first subject based on the set of WBC embeddings, the set of RBC embeddings, and the set of feature embeddings, and a classifier configured to generate the classification using the value.
13. The method of claim 1, wherein the time interval comprises at least one of 6 hours, 12, hours, 24 hours, 2 days, 3 days, 4 days, 5 days, 6 days, 7 days, 30 days, 45 days, 80days, 90 days, 120 days, or 180 days.
14. The method of claim 1, wherein the first subject is at risk of or diagnosed with cancer, wherein the cancer comprises at least one of carcinomas, sarcomas, hematopoietic cancers, adrenal cancers, bladder cancers, blood cancers, bone cancers, brain cancers, breast cancers, carcinoma, cervical cancers, colon cancers, colorectal cancers, corpus uterine cancers, ear, nose and throat (ENT) cancers, endometrial cancers, esophageal cancers, gastrointestinal cancers, head and neck cancers, Hodgkin's disease, intestinal cancers, kidney cancers, larynx cancers, leukemias, liver cancers, lymph node cancers, lymphomas, lung cancers, melanomas, mesothelioma, myelomas, nasopharynx cancers, neuroblastomas, non- Hodgkin's lymphoma, oral cancers, ovarian cancers, pancreatic cancers, penile cancers, pharynx cancers, prostate cancers, rectal cancers, sarcoma, seminomas, skin cancers, stomach cancers, teratomas, testicular cancers, thyroid cancers, uterine cancers, vaginal cancers, vascular tumors, and metastases thereof.
15. A method of training machine learning (ML) architectures to determine values indicating expected conditions of subjects using blood samples, comprising: retrieving, by one or more processors, a training dataset including a plurality of examples, each of the plurality of examples comprising: (i) a plurality of images of a blood sample having Atty. Dkt. No.: 115872-3344 white blood cells (WBCs) and red blood cells (RBCs) obtained from a subject at a time and (ii) a label indicating one of mortality or survival at a time interval relative to the time; identifying, by the one or more processors, from the plurality of images of the blood sample of at least one example of the plurality of examples in the training dataset, (i) a set of WBC images corresponding to the WBCs and (ii) a set of RBC images corresponding to the RBCs; applying, by the one or more processors, the set of WBC images and the set of RBC images to a machine learning (ML) architecture comprising a plurality of weights to determine a value indicating a probability of mortality for the subject at the time interval relative to the time; generating, by the one or more processors, a classification of the subject as one of mortality or survival in accordance with the value indicating the probability of mortality; comparing, by the one or more processors, the classification of the subject generated in accordance with the value determined by the ML architecture with the label of the training dataset; and updating, by the one or more processors, at least one of the plurality of weights in the ML architecture based on comparing the classification and the label.
16. The method of claim 15, wherein at least one of the plurality examples further comprises a non-image dataset including at least one of (i) a plurality of traits of the subject, (ii) a blood count derived from the blood sample, (iii) a first plurality of parameters derived from testing of the first blood sample, or (iv) a first plurality of physiological measurements of the first subject, wherein applying to the ML architecture further comprises applying the non-image dataset to the ML architecture, wherein generating the classification further comprises generating the classification based on applying the non-image dataset to the ML architecture.
17. The method of claim 16, wherein at least one of the plurality examples further comprises the label identifying a presence or an absence of at least one of a plurality of conditions associated with the mortality in the subject, wherein the plurality of conditions comprises a systemic inflammatory response syndrome (SIRS), sepsis, septic shock, bacterial infection, disseminated Atty. Dkt. No.: 115872-3344 intravascular coagulation (DIC), microangiopathic hemolytic anemia (MHA), acidosis, multiorgan failure, anemia, hemophagocytic lymphhistiocytosis, and cytokine release syndrome, and further comprising: determining, by the one or more processors, based on applying the plurality of images to the ML architecture, a second value indicating a probability of a condition of the plurality of conditions associated with the mortality in the first subject, wherein generating the classification further comprises generating the classification to identify a presence or an absence of a condition of the plurality of conditions associated with the mortality in the subject in accordance with the second value, and wherein updating at least one of the plurality of weights further comprises updating at least one of the plurality of weights based on comparing the condition identified by the classification and the label.
18. The method of claim 17, further comprising: generating, by the one or more processors, based on applying the first plurality of images to the ML architecture, a plurality of embeddings used to determine the second value; executing, by the one or more processors, using the plurality of embeddings, a clustering model comprising a plurality of clusters within a feature space, each of the plurality of clusters associated with a respective set of parameters for at least one of the plurality of conditions; determining, by the one or more processors, based on executing the clustering model, an assignment of the plurality of embeddings to a cluster of the plurality of clusters; and identifying, by the one or more processors, from the cluster associated with the assignment of the plurality of embeddings, a set of parameters for the condition of the plurality of conditions.
19. The method of claim 18, wherein at least one example of the plurality of examples comprises the label identifying a plurality of parameters derived from a testing of a second blood sample, wherein the clustering model is established by determining, for each cluster of the plurality of clusters, a respective set of parameters based on the plurality of parameters of the at least one example associated with a second plurality of embeddings assigned to the cluster. Atty. Dkt. No.: 115872-3344
20. The method of claim 15, further comprising: determining, by the one or more processors, a relative score indicating a difference between the subject and a plurality of subjects based on the value indicating the probability of mortality for the subject and a second value indicating a composite probability of mortality of a plurality of subjects; and identifying, by the one or more processors, from a plurality of categories, a category for the subject in accordance with the relative score and a score category for the category.

Description

Atty. Dkt. No.: 115872-3344 AUTOMATED MORTALITY RISK ASSESSMENT THROUGH BLOOD SMEAR IMAGES AND SUBJECT DATA CROSS REFERENCE TO RELATED APPLCIATIONS [0001] The present application claims the benefit of and priority to U.S. Provisional Patent Application No. 63/715,856, filed November 4, 2024, which is incorporated herein by reference in its entirety. BACKGROUND [0002| A computing device may use a machine learning model to process an input to generate an output. SUMMARY [0003] Aspects of the present disclosure are directed to systems and methods of determining values indicating probabilities of conditions of subjects using blood samples. One or more processors may receive a first plurality of images of a first blood sample having first white blood cells (WBCs) and first red blood cells (RBCs) obtained from a first subject at a first time. The one or more processors may identify, from the first plurality of images of the first blood sample, (i) a first set of WBC images corresponding to the first WBCs and (ii) a first set of RBC images corresponding to the first RBCs. The one or more processors may apply the first set of WBC images and the first set of RBC images to a machine learning (ML) architecture. The ML architecture can be established using a plurality of examples. Each of the plurality of examples can include (i) a second set of WBC images of a second blood sample from a second subject at a second time, (ii) a second set of RBC images of the second blood sample from the second subject at the second time, and (iii) a label indicating one of mortality or survival at a time interval relative to the second time. The one or more processors may determine, based on applying the first plurality of images to the ML architecture, a value indicating a probability of mortality for the first subject at the time interval relative to the first time. The one or more Atty. Dkt. No.: 115872-3344 processors may generate a classification of the first subject as one of mortality or survival in accordance with the value indicating the probability of mortality. The one or more processors may may store, using one or more data structures, an association between the first subject and the classification. [0004] In some embodiments, the one or more processors may generate to indicate that the first subject is to survive for the time interval relative to the first time, responsive to the value indicating the probability of mortality not satisfying a threshold. In some embodiments, the one or more processors may provide an output identifying the classification to indicate that the first subject is to survive for the time interval relative to the first time. In some embodiments, the one or more processors may generate the classification to indicate that the first subject is at risk of dying within the time interval relative to the first time, responsive to the value indicating the probability of mortality not satisfying a threshold. In some embodiments, the one or more processors may provide an output identifying the classification to indicate that the first subject is at risk of dying within the time interval relative to the first time. [0005] In some embodiments, the one or more processors may provide, based on the classification, the output and can include at least one of: (i) a notification for a clinician to examine the first subject, (ii) a notification to administer an intervention within the time interval, or (iii) a notification for the first subject to request for medical attention. In some embodiments, the one or more processors may receive a first non-image dataset that can include at least one of (i) a first plurality of traits of the first subject, (ii) a first blood count derived from the first blood sample, (iii) a first plurality of parameters derived from testing of the first blood sample, or (iv) a first plurality of physiological measurements of the first subject. In some embodiments, the method can include applying to the ML architecture further and can include applying the first non-image dataset to the ML architecture. At least one of the plurality of examples can include a second non-image dataset and can include at least one of (i) a second plurality of traits of the second subject, (ii) a second blood count derived from the second blood sample, (iii) a second plurality of measures derived from testing of the second blood sample, or (iv) a second plurality of physiological measurements of the second subject. In some embodiments, the one or more Atty. Dkt. No.: 115872-3344 processors may generate classification based on applying the first non-image dataset to the ML architecture. [0006] In some embodiments, at least one of the plurality of examples further can include the label identifying one of a presence or absence at least one of a plurality of conditions associated with the mortality in the second subject, wherein the plurality of conditions can include a systemic inflammatory response syndrome (SI